1. Field of the Invention
The present invention relates generally to speech recognition applications.
2. Related Art
Speech recognition and its associated software performs a process of converting a speech signal to a sequence of words or text, by means of an algorithm implemented as a computer program. Speech recognition software requires a grammar in order to operate. The grammar is the domain, which represents the valid responses to input prompts. Speech recognition software is most efficient and successful when the grammar is as small as possible. For example, a grammar, which has only two words, “YES” and “NO”, would work very efficiently, especially when used in conjunction with a large number of unknown speakers communicating over a public network.
Geocoding is the process of assigning geographic identifiers (e.g., codes or geographic coordinates expressed as latitude-longitude) to map features and other data records. A geocoder or geocoding application is a piece of software or a (web) service that helps in this process. Current speech geocoding applications typically use grammars that include ZIP codes, and therefore require an individual user to speak or enter a ZIP code to begin the geocoding application.
For example, a speech recognition application can choose a grammar associated with the ZIP code 92109 when the individual speaks “92109” in response to a voice prompt. Choosing such a grammar can have the above-described advantage of reducing the size of the grammar thereby increasing the efficiency of the speech recognition software.
Such solutions are lacking, however, for several reasons. First, a main purpose of speech recognition and associated geocoding is to assist individuals that are in unfamiliar surroundings. For example, an individual might need directions or a location-based service when the individual is on vacation or trying to locate an unfamiliar place. Therefore, in many scenarios where the application of speech geocoding is helpful, the individual does not know the ZIP code related to the unfamiliar location.
Second, grammars that are stored as ZIP codes are not spatially exhaustive, as there are geographic regions in the U.S. that ZIP codes do not cover. Thus, speech geocoding applications based on ZIP codes will not work in all areas.
Third, ZIP codes typically are associated only with addresses, not intersections. A single intersection may be located on the border of four different ZIP codes, for example. Therefore, in such an application, if the speech recognition software does not find a match between the spoken location and a ZIP code grammar on the first attempt (after loading the initial ZIP code grammars), the application would likely move to the next higher level of geography, such as a city boundary. This method of geographic area hierarchy is difficult to maintain and can cause geocoding applications to quickly expand to regions that are too expansive, resulting in an unnaturally large sets of grammars. An unnaturally large set of grammars has a negative impact on speech recognition performance and success as previously described.
Therefore, what is needed is a system and method that reduces or overcomes these significant problems found in the conventional systems as described above.